8.EEE.Latent FULL by Transtellar Publications

International Journal of Electrical and Electronics Engineering Research (IJEEER) ISSN 2250-155X Vol. 2 Issue 4 Dec - 2012 59-64 ÂŠ TJPRC Pvt. Ltd.,

LATENT DEFECTS DETECTION OF ELECTRONIC DEVICES USING QUANTITATIVE THERMOGRAPHY ANNA ANDONOVA Department of Microelectronics, Technical University of Sofia, Bulgaria

ABSTRACT A methodology is presented for early discovery of subsurface latent defects on electronic devices using quantitative thermography. The proposed approach accentuate those thermography sequence differences between the reference and test samples which are likely to be indicative of a defect, relative to differences which are not likely to be related to a defect. The ratios of surface temperatures related to thermographic responses are obtained for two differing non-equilibrium thermal exciters, applied first to a known good device and then applied subsequently to test samples. By using infrared camera SC640 a set-up and developed for that purpose software are used for testing of different LED packages. Some results displaying the effectiveness of the investigation for potential failure prevention are shown.

KEYWORDS: Quantitative Thermography, Defect Detection, Electronic Devices, Reliability INTRODUCTION Early discovery of the latent defects is of increasing concern as producers strive to obtain superior electronic devices quality and reliability. Particularly a need is existed for early discovery of defects which could remain latent or undiscovered for an indeterminate time. Such defects originate potential failures of devices [1]. Qualitative thermograpy has attracted considerable recent attention as one technique of discovering such defects. Infrared hermography (IRT) can be used to measure the temperature of a surface without need for any kind of contact and nondestructively [2, 3]. The infrared cameras can convert this temperature information into color image that represents the temperatures within the scene. It can be used to study non-visible properties of electronic assemblies and packages in the hope of locating devices with potential failure. The patterns of heating effects in a product can be affected by a latent subsurface defect but the heating effect may not be readily detectable for some types of defects. For example, a semiconductor device, prior thermography techniques have been only marginally effective in locating defects, except in certain limited situations. More available thermal analysis methods in practice use image subtraction [4, 5]. The purpose is to remove features from the difference image which are known to be normal, so as to increase the likelihood that any residue in the difference image is indicative of a defect in the product [6]. Thus, if the thermogram for a known non-defective sample is subtracted from the thermogram for a device being tested, the difference, if any, are hoped to be indicative of a defect. The greatest preferences in the prior techniques have been for devices which produce relatively little heat. However, products such as insulated-gate bipolar transistors (IGBT), light-emitting diodes (LED), transistor-transistor logic (TTL) circuits etc., which produce much heat have yielded marginal success in diagnostic testing using prior classical image subtraction techniques. The problem appears to be that the variability of heating among non-defective samples can be much larger than the effect upon heating produced by products

Anna Andonova

having a subtle defect. This tendency makes such defect difficult, if not impossible, to detect by previously known image methods. In this paper is proposed approach to overcome the limitations of image subtraction thermography. It is emphasized those thermographic differences between a reference sample and test sampled which are likely to be informative of defect, relative to difference which are not likely to be related to a defect.

DEFECT DETECTION APPROACH The design and production process tolerances of a product are the main reason because of some thermal variation may be observed between different non-defective samples. This thermal variation for electronic components and circuits have both a linear and non-linear ohmic-component with respect to applied voltage and is also subject to any dynamic stimulation applied. The amount of temperature rise above ambient temperature is related linearly to the power dissipated. Thus, the temperature rise expected to be observed has got internal and external nature. Internally this rise is a function of packaging and linear and non-linear resistances. External reason is that the temperature rise is a function of the applied voltage and dynamic stimulation. Latent subsurface defects are difficult to discern from normal variation of the temperature. A defect can manifest itself as modification of the internal resistive structure of the electronic device or circuit. Resistance can be modeled as a function of applied voltage U R(U) = 1/βUα

(1)

where β is a constant, in the case of integrated circuit or electronic component, micro-electromechanical system (MEMS) etc, due to the packaging thereof. The relationship (1) being non-linear to a degree dependent upon the degree to which α deviates from zero. Defects also have a strong tendency to be function of the external variables which differ from those function exhibited by their normal counterparts. It is well known from the practice of integrated circuits design, that the ratio of the same name type of magnitude (for instance resistance) can cancel or eliminated expected variations of the output characteristics. On this way what is left is an image highlighting only those pixels of the thermogram for which the reference sample and the test sample exhibit substantial differences in their ”non-linear” characteristics. The temperature rise T above the ambient of a sample is equivalent to the power dissipation P ∆T=T(U)-Tα=cpP(U)

(2)

where cp is a constant related to the packaging and heat-sinking of the tested sample. T(U) is the elevated temperature produces by applied voltage U. It may also be written ∆T=T(U)-Tα= βcpU2(dI/dU)

(3)

where β is a linear term - see equation (1). If the resistance is a linear function to the applied voltage, than the first derivate dI/dU will be a constant value. On the Figure 1 is given a bar diagram showing temperature relationship for comparable points for a reference sample (REF) and test samples without defect (NF) and with defect (F), respectively. Let consider a non-defect sample – reference object. For applied voltage U1 (corresponding to 95% from the specified nominal power value), the elevated

Latent Defects Detection of Electronic Devices Using Quantitative Thermography

temperature of the reference sample will be ∆T1r and for voltage U2 (corresponding to 105% from the specified nominal power value) the elevated temperature will be ∆T2r: ∆T2r=T2r(U2)-T1r(U1)=βcpU22(dIr/dU2)

(4)

where ∆T1r=T1r(U1)-Ta. The ratio Rr is Rr=∆T2r/∆T1r =U22U12[(dIr/dU1)/(dIr/dU2)]

(5)

Let consider the result for a sample without defect – object 1 on the Figure 1. If infrared images are taken at the same two voltage levels U1 and U2 a temperature difference ratio ∆T2ob1/∆T1ob1 =Rs1 can be derived. Taking ratio of the ratios and taking account of equation (1) it can be written Rr/Rs1=(U2/U1) (αob1-αr)

(6)

Thus the resultant image will be a function only of the applied voltages and the difference in the non-linear component of the internal resistances. That the latent defect or detectable differences exist at the array of data points for αob1≠αr when αob1≠0 and αr≠0

(7)

If the non-linear resistances are equal, then the result goes to unity. In the case of a defective sample, the original temperature components Rs2, Rs3 and Rs4 from Figure1 can be considered to be identical to the scenario relating to nondefective sample – object1. On the Figure 1 are illustrated the four typical relationships for comparable pixels of the thermographic images.

DECISION CRITERIA A sensitive measure of non-linear defects because the small, but readily variable, non-linear term is not swamped by larger linear variations, which have cancelled out. During the thermographic measurements the applied voltage is hold constant and only the dynamic exciters are changed, usually by turning a clock on or off. In this case the heat generated by defect will depend upon how the duration of a thermal exciter is changed as well as upon its magnitude. An estimate of the available latent defect F can be made as illustrated on Figure 1 by assuming that F modifies the values of ∆T1obi or/ and ∆T2obi for the i-th sample as follow

∆T2ob2, then ∆T2ob2= ∆T2ob1+F2, where F2 = ∆T2ob2 -∆T2r∆T1ob2/∆T1r

∆T1ob3, then ∆T1ob3= ∆T1ob1+F3, where F3 = ∆T1ob3-∆T1r∆T2ob2/∆T2r

(8)

(9)

∆T2ob4, then ∆T2ob4= ∆T2ob1+F4`, where F4` is calculated by eq. (8) and ∆T1ob4, then ∆T1ob4= ∆T1ob1+F4``, where F4`` is calculated by eq. (9) and F4 = F4`+ F4``

(10)

for the non-defective sample (Rr/Rs1=1) F1=0

(11)

Anna Andonova

On the basis of equations above, an estimate what should be, based on measured values of ∆T1r, ∆T2r, ∆T1obi and ∆T2obi (indices r and i show the reference item and the serial number of the tested samples) can be made.

EXPERIMENTAL VERIFICATIONS The proposed methodology was verified for many tested samples of LED. After that the identified as defective devices are analyzed in order to reveal the latent defects by other destructive methods. Figure 2 shows the block diagram and the interrelationships among the items of equipment used in the experimental set-up.

Figure 1: Bar Diagram of Temperature Relationships for Comparable Pixels

Figure 2: Block-Diagram of Used Set-Up

Latent Defects Detection of Electronic Devices Using Quantitative Thermography

For testing devices mounted on board not on fixture, the technique could be modified to obtain data only at component (e.g. passive or active devices) locations. Based on defined decision criteria the test samples can be accepted as having no defect or rejected as having a defect of sufficient magnitude based upon the statistical analysis of the composite record. The composite record is formed from the ratio record and the unused difference records, which for example, may show one more pixels deviation from the surrounding background by one or more standard deviations. A defect indication is generated when the composite record yields a statistically significant deviation from expected value. Dual-level voltages applied to power the device under test (DUT) are applied. The minimum and maximum voltages specified for particular LED is used. The dynamic test includes full simulation of the expected operation if timing constraints of the thermal ratio are compatible therewith. Proper testing technique requires controlled air flow in the thermal box to maintain ambient temperature and to thermally stabilization. Near the device under test and the used fixture should not to have any heat source. The areas that experience large thermal shifts from air conditioning are avoided. Materials inside the thermal box have got high emissivity. Image enhancement, noise filtering, relevant smoothing between adjacent pixels of each thermal ratio image and for the resulting comparative image that will tend to filter speckle noise are performed. Another image enhancement [7] can be performed with the original elevated temperature images. One or more mask images for spatial filtering are generated according to the device specificity. The used algorithm for image processing and analysis of the error factors are not discussed in the present work. A dynamic test threshold that maintains a fixed probability of falsely indicating a good LED or not detecting a defective one is set for any given size of component in terms of number of image sequences. Some indicative result of LED with and without latent subsurface defect are shown on Figure 3 and Figure 4 for comparison.

Figure 3: Images of a LED Package without Latent Defect

Figure 4: Images of a LED Package with Subsurface Latent Defect The cases (a) on the Figure 3 and Figure 4 respond to row thermogams of the tested LED. The cases (b), (c) and (d) are received after image contrast enhancement, relevant smoothing between adjacent pixels of each thermal ratio image and generated mask images for spatial filtering, respectively. The tested LED package from Figure 4 is classified as a good device by functional test, but proposed thermography test shows availability of subsurface latent defect. This can be a cause for early failure appearance in work conditions.

Anna Andonova

From 1000 LED packages of Bulgaria's Octa Light PLC tested by proposed thermography method only for two devices are not find latent defects after next destructive failure analysis.

CONCLUSIONS The results clearly show that the normal thermal variations cancel when ratio of two array of infrared image differences are derived for measurement from the same simple or from the reference sample and the test sample. The measurements can be formed into a composite record including two other arrays on infrared image differences, for corresponding data points in each array, for the reference sample and test sample. Experimental set-up is designed and used for subsurface latent defect detection for LED package. A defect is indicated when an anomalous region appears in the composite record. The offered approach is particularly useful for detecting hidden subsurface defects in electronic devices. On this way auspicious potential failure prevention can be achieved.

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